D-Wave - Qubits Miami | Event Takeaways, Key Lessons, and Major Developments

Quantum Computing is here, and is only getting better.

D-Wave Systems hosted an amazing event from Tuesday January 17, 2023 - Thursday January 19th 2023 bringing some of the smartest minds in advanced computing together with some of the most innovative industries deploying quantum computing solutions.

The crux of the conference was summarized by D-Waves CEO Dr. Alan Baratz

Dr. Baratz opened up the conference by explaining that the very idea of quantum computing is intimidating for many outside of academia. One of the major themes of the conference was to address a common reaction to quantum computing known as FUD:

Fear, uncertainty, and doubt.

Deloitte's Kate Abrey, in her presentation with Scott Buchholz, summarized the commercial challenges perfectly in their presentation:

“The minute you say quantum - the dynamic of the conversation changes”

Scott, in turn, suggested that the way to get past this conceptual bottleneck is utilize tools already commonplace in hardware, software, and digital specializations. Paraphrasing his words here:

One of the most important jobs we can do is develop proofs of concept, prototype, iterate, and work with mental models.

This summarizes the purpose of the Qubits conference perfectly. To situate attendees (online and in person) with real world examples of quantum solutions, and the science behind it.

Scott and Kate with a "make it make sense" presentation. Quantum anything is very intimidating without subject matter expertise.

Qubits did not hold back on highly scientific, mathematics and physics heavy keynotes, discussing items like:

  • hardware capabilities of quantum computers
  • methods of problem solving using quantum annealing
  • methodologies for achieving desirable quantum states on quantum machinery
  • and much much more...

On top of industry examples and hard science, Qubits also provided a roadmap for many of D-Wave's key milestones in the years to come.

We try to attend (and encourage others to attend) these events to familiarize ourself with necessary concepts through immersion in this unique language and its technological protocols. As always, we're dedicated to sharing and disseminating trade terminology to make these concepts more accessible to our industry peers.

Did you know?

A big reason for writing this in such detail is to provide some pre-reading for the Quantum Miami event on Jan 25-27th. Study up, and can't wait to meet many of you!

Quantum Annealing vs. Gate Model Quantum Computing

D-Waves industry advantage comes from their pursuit of commercializing Quantum Annealing systems "now" as opposed to waiting for the future of gate model quantum computers, one of the holy grails of the industry. As there is a lot of conceptual detail to unpack in these sentences, I've trusted my friend ChatGPT to distill the concepts for straightforward readership:

Quantum annealing is a method for solving optimization problems by encoding them into the energy levels of a quantum system. Gate model quantum computing, on the other hand, is a method for performing general quantum computation.

Furthermore, a huge part of D-Waves advantage comes from providing solutions in the space of quantum annealing. This is because quantum annealing is substantially faster to solve optimization problems than what is known as classical computing.

In quantum computing, annealing refers to a process of slowly reducing the energy of a system to find the global minimum energy state, which corresponds to the optimal solution for a given optimization problem. In classical computing, annealing is a technique used in simulated annealing, a heuristic optimization method that simulates the process of slowly cooling a material to find its lowest energy state. The key difference is that while quantum annealing uses the principles of quantum mechanics, classical annealing uses a classical algorithm.

Understandably, this terminology can be hard to absorb at a first glance. It's very important to see the language as just that - unfamiliar. Unfamiliar or not, I am not sugar coating when I say that the technology itself is completely mind blowing.

D-Wave has positioned themselves in quantum annealing space today, with plans to embrace gate model quantum computing in the years to come. As Dr. Baratz put it:

Most of the problems that businesses need to solve are optimization problems.

To make matters straightforward, fun, and inviting, Dr. Baratz started off the event speaking with industry stakeholders wearing a number of hats or costumes.

The purpose was to illustrate how applicable the technology is in the real world. In the tweet image above, he is wearing his "financier hat", while in the one below he exemplifying quantum computings role in infrastructure development.

After illustrating associations between D-Wave systems and industry partners through costume changes, a series of keynotes presented how the quantum annealing solutions applied to their businesses.

Unfortunately while I cannot share slides from every presentation, it's worth noting the ones shared in this article are here to get the point across on practical avenues for modern quantum computing.

Real time multivariate computational optimization is messy, hard, and important.

SLB showed us exactly how crucial optimization systems were in their operations.

SLB needs to have their data optimized because there's simply too much of it.

Their presentation showcased a number of different aspects of their operations. Without getting into too much detail, it could include anything from:

  • building an energy system from step one
  • managing a network of energy facilities
  • staffing facilities from different roles and seniority levels
  • extracting energy from a variety of in water, and underground, depts
  • and much more..

Being able to see how quantum annealing is applied to complex systems like SLB's example helps explain the point of optimization in computing. We can generalize this idea a bit further by saying:

When operations scale to become more complex, there are going to be more variables to deal with.

As more variables continue to generate more data, computational tools exist to optimize these for the organization.

Mastercard needed to optimize for loyalty and rewards across a high number of currencies, counties, transactions, and customers.

In the financial technology world, there were numerous examples of how cumbersome offers and payments get at scale. We're talking about astronomical amounts of transactions with faster rates of transactions.

If there's not an optimization protocol for this, organizations can introduce any number of complexities, setbacks, cost associated problems, or more.

GoodLabs studio, another fintech company, needs annealing for liquidity problems.

One thing that caught my attention was the differences between

  1. high customer volume, high transaction rate payment processing (Mastercard), and
  2. low customer, high value payment prioritization in something like liquidity utilization problems.

It was very helpful to understand that even within one industry like fintech, there are a range of applications for computational optimization. One could only assume this is the tip of the iceberg.

Johnson&Johnson showcased how loading trucks with optimization systems could maximize items like shipping costs.

Let's be honest, trying to make sense of quantum annealing on the best of days can be a challenge for many. Having straightforward examples of the process, and taking the time to wrap ones head around the science is a great way to process the information.

⚠️ the tweets below showcase ways of wrapping ones head around annealing systems in context

There were numerous times during the presentation where I brainstormed how this would work in my own terms. I do this by imaging what these systems would solve for in ridiculous contexts like the shipping and logistics requirements of getting cupcakes to birthday parties.

So we need to ship cupcakes to birthday parties across continental North America...🤔 

Trying to play around with the subject matter is one way that I am able to learn. Perhaps it shows in the following tweets, imaging the on-ground reality of something like 3D Cuboid Loading optimization.

Savant X's logistical optimization systems were particularly fascinating, and a great visual example of annealing.

I extended this 3D cuboid loading cupcake metaphor from a single vessel, into the next presentation about shipping terminal optimizations. Essentially the 3D cuboid loading problem for a single delivery truck would need to be abstracted into the sorting and loading of ALL moving trucks, dispatched to multiple birthday parties, from a singular point of origin like a shipping doc.

⚠️ the tweets below showcase ways of wrapping ones head around annealing systems in context

Where I am going with the cupcake analogy, however, is of more significance.


Most of the problems that businesses need to solve are optimization problems.



fear, uncertainty, and doubt are deterrents for participation, and utilization, of quantum annealing systems in the real world. I don't particularly blame myself for trying to interpret the conference at points in terms of birthday pastries.

Referring back to Deloitte's Scott Buccholz again, regarding the need for effective mental models in quantum annealing, why wouldn't we try to associate something complex with something delicious. After all we do want mental models to understand how the variables fit together...

⚠️ the tweets below showcase ways of wrapping ones head around annealing systems in context

and with the POWER of quantum annealing... ⚡

Did I go a bit overboard?

Yes. But it was worth it. If I can make advanced computational optimization systems humorous then my work here is done.

Medical Industry Implications of Quantum Computing

It was most certainly a relief to hear that many industries struggle with paradigms of choice and selection on tool adoption in the space of artificial intelligence, quantum infrastructure, and charting a course for solutions generally.

⚠️please note - typos in tweets were a function of rapid transcription, for use later on. Apologies for the typos, brevity and lack of nuance...

I can't imagine writing a lookup function to a genomic library, let alone optimizing them for research. I have to assume it takes a fair degree of specialization to excel here. Again, to avoid embarrassing myself I can only say hats off to those who are figuring out the tricky bits.

Brian Martin's presentation showed that resolutions for issues in life sciences are also not solely a function of technical chops. They appear to be a multi-layered, and highly integrated, series of institutional, choice based, expense/profit determined, bureaucratic, and ultimately rather chaotic variables that need to be ironed out.

Scheduling and Prioritization

Benny Wai of Pattinson Food Group illustrated some more pragmatic examples a company like Acorn could support. If you think about the journey of a traditional e-commerce company transitioning from a bootstrapped phase into a large organization, setting up data systems and planning ahead really matters.

People tend to underestimate the complexity of logistics, staffing, scheduling, fulfillment, let alone food spoilage in e-commerce based applications. While shortcuts do exist here, in more cases than not, we cannot discount the effort required to automate, then optimize, highly specific aspects of shipping and fulfillment.

It's great to see how D-Wave technology fit into a broader shipping and fulfillment technology stack.

Iterative projects leverage a number of tools and toolchains to produce a result. Seeing this mapped out, while fairly standard in some fields, is always just nice.

It's really important to see trailblazers in these fields. Trailblazers give us a frame of reference to explain our own software/product evolution to customers, critical paths, contextual decision trees, etc from their examples. In our work, Acorn may find itself at point with a Shopify client having to say:

In order to ensure accuracy in your shipping and fulfillment, we need to use computational optimization systems to ensure that you're not wasting money, time, or personnel efforts on redundancies.

It's extremely important to be able to network with individuals to chat about technology and related solutions. Also, it's fun.

It's also important to see how something like scheduling optimizations served as an intermediary between technical specifications on the one hand, and success criteria on the other.

It's exciting when there are presentations showing real world examples of the types of deliverables we try to produce for information architecture and user experience designs.

Route planning for congestion and disaster responses

Route planning is the process of finding the most efficient or cost-effective way to travel from one location to another. Optimization plays a crucial role in this process, as it allows for finding the best route given certain constraints and objectives. This is typically done using mathematical algorithms and models that take into account various factors such as distance, time, traffic, and cost. The goal of the optimization is to minimize the total travel time or distance while maximizing the overall efficiency of the route.

In Quantum Technology Innovation hub, Tohoku University, Masayuki Ohzeki, Founder, Sigma-i, and Professor, Tohoku University explained the role of university based quantum hubs creating integrated solutions to complex problem sets using annealing technologies.

Quantum hubs in universities are research centers or groups that focus on the development and application of quantum technologies. They typically bring together experts from various fields such as physics, computer science, and engineering to conduct interdisciplinary research and development on topics such as quantum computing, quantum communication, quantum cryptography, and quantum simulation.

They aim to accelerate the progress of quantum technologies by fostering collaboration and providing resources and infrastructure for research.

Another example of quantum hubs came from CEO Damir Bogdan of uptownBasel Infinity. From their website:

uptownBasel is an international centre of excellence for Industry 4.0... We are creating a development and production location that will be home to selected technology companies and other organizations. The focus is on industrial manufacturing, healthcare and logistics, as well as digitalization as a horizontal function.
By introducing companies to potential partners, it will help to bring embryonic ideas to life in the fields of robotics, the Internet of Things, artificial intelligence, future mobility and agile working.

To summarize

The conference brought together science and technical minds, D-Wave company personnel, industry partners, and academic representatives to showcase quantum annealing solutions and next steps. The point of all of this is to bolster engagement and adoption of quantum technology.

Adoption is costly, time consuming, and requires complex decisions to be made

This is why we prefer to start our projects with a thorough discovery process before we initiate work on the software infrastructure. This allows us to have well distilled technical specifications and highly organized success criterium.

Bob Sorensen of Hyperion Research did a great job explaining the adoption of quantum computing in professional contexts and associated bottlenecks.

Bob had one of my favourite quotes of the conference regarding the deployment of D-Waves Quantum Annealing System.

Don't look at quantum as an island. Look at it as a tool in the the advanced computing toolbelt... quantum is a part of an overall computing composition.

It's for reasons like these we try to use our blog to showcase some of these major, and emerging, themes in advanced computing. The link below is another post we did on artificial intelligence and machine learning.

All part of the overall computing composition of advanced technologies, after all 😉

We need to have a better language to define quantum systems in business

At this time, it's generally IT professionals who influence the decision to migrate into quantum computing. The underserved areas of the "QC Influencer" stats:

  • programmers
  • program manager
  • computer scientist
  • scientific research/SME
  • non technical management

In our experiences these are people who either are not authorized to make decisions like this, or are not heard as organizational advocates to build infrastructure to degrees where a QC based solution would become practical.

Unique businesses have unique challenges. Helpful to present the data around where there is traction.

As for the hurdles of QC adoption he spoke about:

  • lack of in house expertise
  • lack of funding
  • low business priority
  • complexity of integrating into existing IT stacks

One could say this is exactly why we write this articles in the first place. Identify where we can support, connect dots, share field examples, and ultimately talk about the issues themselves - and the benefits of adopting quantum computers in the future.

Understanding what tools exist, for what reasons, to solve what problems is exactly what Bob Sorensen was referring to with overall computing compositions.

Beyond the need to understand the roles of each tool within the overall computing composition, we also need to understand the business incentives that exist to support these efforts.

We need to pay for advancing these technologies.

At the bottom of the document linked below, you can see a cursory list of investors Acorn accumulated at a webinar last summer.

We need to make it easier to align the various moving parts between tools, technologies, and investors through information synthesis. For complex problem solving, odds are we'll eventually need some mixture of AI, ML, Quantum Computing, and money.

Of course, we always advise on crawling before one can walk on these matters. Having an approach that factors in details like:

  1. Minimum requirements to submit a pitch deck to investors
  2. Caveat Analyses for Technical Implementations
  3. Connecting Industry Related Subject Matter Experts
  4. Data evolutions from Devops, to MLOps, to AI, to quantum optimizations

Evolutionary and iterative approaches to software projects are strategies Acorn seeks to build out with industry partnerships as a service provider.

Nobody can do this work alone.

Working in an evolutionary way, and planning for obstacles, are ways we can build iteratively into sophistication in software and systems design.

A hat tip and bow to the real contributors of the D-Wave conference

As mentioned before, none of these tools exist without the brilliant minds of researchers, scientists, mathematicians, and hardware professionals driving the evolution of the technologies.

Qubits had a sizeable component dedicated to showcasing the "how" part of building quantum machinery.

In the talk Iterative Quantum Optimization: University of Minnesota Alex Kamenev Director of FTPI, Professor explained to attendees highly specific mathematics allowing the machines to perform.

While this article explores the suite of commercial opportunities available from these systems, these amazing computational compositions can never happen without amazingly talented, bright minds.

Without hard mathematics and quantum science, there are no commercial opportunities to explore.

In order to drive product evolution, these events have a mixture of specialized personnel reporting, and disseminating science and capabilities. Personally, I found it interesting to see who would gravitate towards which speaker series. There was no homogenous attendee at Qubits. Some where underwhelmed by the commercial applications and were excited to see the maths and science be explored. Others, of course, needed real world demonstrations to understand how to position their organizations for quantum.

Quantum is an ecosystem, like any computational suite.

It's a multidisciplinary endeavour factoring in a number of different skills. Mathematics plays a crucial role in the theory and implementation of quantum computing. Quantum algorithms are often represented using mathematical structures such as matrices and vectors.

Many attendees are at the conference explicitly to gather these details.

Quantum states and measurements can also be described using linear algebra. The mathematical framework of quantum mechanics, including concepts like superposition and entanglement, is used to model and analyze quantum systems.

Additionally, mathematical techniques from areas such as optimization and error correction are used in the design and implementation of quantum algorithms and error-correction codes.

All I can say is "Bravo."

And cordially tip my hat.

Thanks to D-Wave for hosting the event and sharing their findings. There's a lot to learn in this space but in doing so, it unlocks a ton of possibilities and value for organizations and institutions.

Full Event Breakdown and Speaker List

Day 1:


Dr. Alan Baratz, CEO, D-Wave and Selected Guests from Mastercard, Deloitte, Davidson


​Quantum for Finance: Mastercard

Steve Flinter, Vice President​

Quantum Computing in the Energy Industry

Industry Speaker​

Exploring Quantum Computing to Solve 3D Cuboid Loading Problem: Johnson & Johnson

Nitish Umang, Senior Manager

Optimizing Logistics with Quantum: Supply Chain Issues Solved: SavantX

Ed Heinbockel, CEO and David Ostby, Co-Founder and Chief Science Officer

TV Commercials Allocation for Frequency Optimization: Recruit

Kotaro Tanahashi, Machine Learning Engineer


Quantum Computing's Enterprise Value and Opportunities: Hyperion Research

Bob Sorensen, SVP Research, Hyperion Research

​​D-Wave Advantage Keynote: The Next-Generation of Quantum Technology and Systems

Mark Johnson, Senior Vice President Emile Hoskinson, DirectorD-Wave Leap

Keynote: The Next-Generation of Quantum Cloud Software, Services, and Tools

Murray Thom, Vice President 

Innovation in CQM: Feature Selection Demo: D-Wave

Alex Condello, Senior Director


Coherent Quantum Annealing for Simulation and Optimization: D-Wave

Andrew King, Director

Iterative Quantum Optimization: University of Minnesota

Alex Kamenev, Director of FTPI, Professor


Kate Abrey, Principal; Scott Buchholz, Global Quantum Lead​

Day 2


Six-time Emmy winner for his stories on CBS Sunday Morning, a New York Times bestselling author, a five-time TED speaker, and host of 20 NOVA science specials on PBS  


​Panel: Building and Training Quantum Teams

Florian Neukart, Chief Product Officer, Terra Quantum; Venkat Kasirajan, Head of Quantum Technology Research, Trimble; Brian Smith, Lead Software Engineer, State Farm. Moderated by Mark Snedeker, SVP Growth, D-Wave

​Assessing Applications for Hybrid: D-Wave

Alex Condello, Senior Director, D-Wave​Break

E-Comm Driver Auto Scheduling: Pattison Food Group

Benny Wai, Manager, Analytics Development

​Portfolio Optimization with Quantum Annealing: Machine Learning Reply

Johannes Oberreuter, Technical Lead, Quantum Computing Practice​

Quantum Use Cases for Life Sciences: AbbVie

Brian Martin, Head of AI

​​Real-Time Quantum Liquidity Optimizer for Wholesale Payments: GoodLabs Studio

Thomas Lo, Christopher McMahon, Donald Mcgillivray, J.P. Lam​​Lunch

Workshop: Quantum Benchmarking 101 (in-person conference only)

Quantum Technology Innovation hub: Tohoku University

Masayuki Ohzeki, Founder, Sigma-i, and Professor, Tohoku University

Quantum Hub Innovation: uptownBasel Infinity

Damir Bogdan, Chief Executive Officer


Benchmarking Quantum Annealing: Los Alamos National Lab

Carleton Coffrin, Staff Scientist

Quantum Benchmarking for Business: D-Wave

Catherine McGeoch, Principal Scientist