U.S. Department of Energy OTC: GIC 2026

As electricity demand grows and the U.S. power system integrates data centers and large industrial loads, grid planners must strategically determine where to deploy energy storage systems and microgrids to maximize resilience, reliability, and economic efficiency over multi-year planning horizons. These siting and sizing decisions must account for load variability, generation variability, transmission constraints, contingency requirements, and varying weather risks, requiring evaluation of thousands of potential infrastructure configurations across diverse operating conditions. Participants will investigate quantum formulations of siting decisions, develop mappings to QUBO or variational optimization frameworks, and benchmark hybrid quantum approaches against established classical planning solvers. The objective is to determine where quantum methods may improve combinatorial search efficiency, scenario exploration, solution robustness, or investment trade-off analysis for critical energy infrastructure.

Final Phase: In Phase 3, participants will transition to full execution and validation, running your models on the selected platforms and analyzing outcomes in depth. Deliverables can include performance benchmarks, simulation accuracy metrics, and comparisons to classical baselines. Finalist teams should try to demonstrate how their quantum-inspired solutions uncover higher-order patterns, accelerate computation, or provide insights that are difficult to achieve classically. Submissions could also include documentation of technical challenges, mitigation strategies, and reflections on broader applications, ranging from materials innovation and financial decision-making to brain research and algorithmic performance. This final phase will test each team’s ability to combine quantum innovation with domain-specific impact for scalable solutions with practical relevance.
Submit your Phase 3 submissions via the Aqora Competition page for each of your chosen Challenge(s) due 11:59 PM (EST) on Sunday, July 26. Important: One submission is required per team and must match submission criteria or risk disqualification.

QC Credit Allocation and Instructions
Based on your Phase 2 submission, the Organizing Committee is creating a team account on qBriad with an allocation of credits. Depending on your allocation, credits can be used to access both classical (CPU/GPU) and quantum (QPU) computing resources on the platform. Please visit qBraid to learn more about getting started: https://docs.qbraid.com/home/introduction
For those who expressed interest in D-Wave and QCi device access, the POC who completed the team TypeForm will receive information about getting access for your team. IBM device access can be found on the homepage of Aqora.

Summary: Top Actions for All Participants
Regardless of which challenge track you are competing in, the following actions will most directly improve your Phase 3 score:
• Run your code (if applicable) on qBraid, QCi, D-Wave, or IBM before submission: Judges will re-run your code exactly as submitted. A result that cannot be reproduced will not receive credit for that result.
• Report a classical baseline: Every challenge requires a comparison against a non-quantum method on the same problem instance. This is the single most common gap in Phase 2 submissions.
• Demonstrate and explain quantum advantage: Develop your Phase 2 platform and resourcing plan to demonstrate how you used QC or simulators to provide benefits over classical approaches. Expanded appropriateness of selected QC or simulator, qubit/depth/shot estimates, and integration. Detailed write-up of concrete results, observations, and conclusions. This is a critical Phase 3 judging criterion.
• Be specific about numbers: Present your results. Qubit count, circuit depth, shot budget, wall-clock runtime, and key metric values must appear in your write-up. Qualitative descriptions of performance are not sufficient.
• Write a complete README.md: The README is the first thing judges read. A clear, step-by-step README that lets a judge reproduce your result is worth more than an extra paragraph of theoretical justification in the write-up.
• Be honest about limitations: Every challenge rubric rewards understanding of where the quantum approach does and does not provide benefits. A clear discussion of limitations paired with a working implementation will outscore an overstated claim paired with a partial one.

Specific requirements are outlined in the Challenge Documents on the Challenge pages.

Generally, your submission should try to address:
·       Focus area and rationale
·       Technical approach to quantum integration
·       Stakeholder relevance
·       Data modelling strategy
·       Quantum platform justification and resource needs – details for accessing can be found below – selection details coming soon.

Important: Non-compliant submissions due to non-compliant page limit, use of the Cover Page template, or use of AI may be disqualified and voided.

As electricity demand grows and the U.S. power system integrates data centers and large loads, grid planners must strategically determine where to deploy energy storage systems and microgrids to maximize resilience, reliability, and economic efficiency. Optimal siting decisions must account for load variability, generation variability, transmission constraints, contingency requirements, and varying weather risks, while balancing capital investment and operational performance over multi-year planning horizons.

Energy storage and microgrid placement problems are typically formulated as large-scale mixed-integer optimization models with nonlinear power flow constraints and multi-scenario uncertainty. These problems require evaluating thousands of potential infrastructure configurations across diverse operating conditions, making them computationally intensive and increasingly complex as fast-varying loads proliferate.

This challenge explores how near-term quantum computing approaches—particularly hybrid quantum-classical optimization and heuristic methods—can enhance strategic infrastructure planning for storage and microgrid deployment. Participants will investigate quantum formulations of siting and sizing decisions, develop mappings to QUBO or variational optimization frameworks, and explore hybrid decomposition techniques for handling multi-scenario resilience constraints.

Teams will benchmark quantum and hybrid approaches against established classical planning solvers using realistic grid test systems and resilience-focused case studies. The objective is not to replace classical tools, but to determine where quantum methods may improve combinatorial search efficiency, scenario exploration, solution robustness, or investment trade-off analysis.

Outcomes of this challenge have direct implications for accelerating new load integration, improving grid hardening strategies, enabling community-level resilience through microgrids, and strengthening U.S. energy security and infrastructure competitiveness.

Questions? Please email quantum@connecteddmv.org

Congrats, challengers, on your journey so far! Now comes the fun part -- running your solutions on real quantum devices.
Challenge finalists (Phase 3) receive access to the following platforms and systems: IonQ AQT, Cepheus-1-108Q, IQM Emerald, IBEX Q, and more. Full list available here once registered: https://account.qbraid.com/devices

If you're using qBraid, all you need to do is click the ‘Launch on qBraid’ button found on Aqora to link your Aqora account with your qBraid account.

Then you should be taken to the challenge page on qBraid (see image). Click Launch on qBraid, and you will have the challenge info cloned into your qBraid account.

As we process device usage requests in the Phase 2 downselect and finalist team selection, we will load your account with credits!

This challenge requires optimization tools which can handle highly nonlinear models. Quantum Computing Inc (QCi) created an analog computing paradigm known as Entropy Quantum Computing (EQC). This paradigm is currently implemented in an electro-optical hybrid system known as Dirac-3. Differentiating it from other analog computing platforms, Dirac-3 offers full connectivity among problem variables with up to fifth degree polynomials. The device also allows the use of two machine-level encodings. One is a discrete encoding- integer valued variables. The other is a quasi-continuous encoding. This flexibility gives the user the ability to solve a wider variety of problems with tighter approximation. While other platforms could be useful for certain aspects of this challenge, Dirac-3 covers the optimization gamut.

QCi’s Dirac-3 - https://quantumcomputinginc.com/products/commercial-products/dirac-3
Dirac Quick Start - https://quantumcomputinginc.com/learn/developer-resources/entropy-quantum-optimization/dirac-quick-…
eqc-models - https://pypi.org/project/eqc-models/
eqc-models-tutorial - https://github.com/qci-wdyk/eqc-models-tutorial
QCi Resource Library - https://quantumcomputinginc.com/learn

D-Wave offers a variety of free and paid trainings, e-books, and webinars. A few of the most relevant ones are listed below:
• Quantum Fit webinar: https://youtu.be/8ewcmvYDRV8?si=mQGiBpK8jjY7Vd6S
• Quantum Fit panel from Qubits 2026: https://youtu.be/E4aJGmsNvog?si=k9MNIH13RpM5YtRL
• Quantum Fit eBook - this is an asset that we offer for Commercial organizations, but could be used by your applicants too, to assess if their project would benefit from using our platform: Is Quantum Optimization a Fit for Your Business?
• Free Introduction to Quantum Computing course (if any participants need to understand annealing vs gate and use cases for each): https://training.dwavequantum.com/product?catalog=IntroductionQuantumComputing
• Getting Started with Applied Quantum Optimization course covers problem discovery and problem formulation, this is a paid training, the price is $50. https://training.dwavequantum.com/product?catalog=GettingStartedAppliedQuantumOptimization
• Additional, more advanced trainings can be found here: D-Wave | Courses

• GIC organizers will provide D-Wave access and provide the users of the finalist teams access to the platform.
• Users will be able to access either QPU or D-Wave’s Hybrid Solvers
• There will be a set time limit and credits determined by the organizing and judging committee.
• There is an important limitation on access; the users need to be in a so-called "Leap Approved" country. Please find the list of these countries here: From What Countries Can I Access D-Wave's Leap Quantum Cloud Service? – D-Wave Quantum Inc
• Please indicate you meet these needs in the Typeform.

As highlighted in IBM’s recent updates, the Open Plan has been significantly expanded and is intentionally designed to support early stage exploration, prototyping, and algorithm work.

In particular:
• The Open Plan is free and publicly available.
• Eligible users can unlock expanded runtime (up to 180 minutes over 12 months) once a modest usage threshold is reached, allowing teams to scale experimentation as their work matures.
• This expansion is structured so that additional access becomes available at an appropriate usage trigger point, without special forms, approvals, or event specific onboarding.

IBM believes this model better aligns with the likely needs and maturity of participating teams.

For participants seeking details on platform capabilities and access, these should be the primary references:
• https://www.ibm.com/quantum/blog/open-plan-updates
• https://www.ibm.com/quantum/blog/whats-new-q1-2026

Any questions about accessing or using IBM Quantum Open Plan should be directed to IBM.