JIJ releases SQOA solver to handle more variables than qubits, raising questions over IBM-dependent delivery model

【Article Title】 JIJ releases SQOA solver to handle more variables than qubits, raising questions over IBM-dependent delivery model 【Article Body】 AI TRANSLATION This article is an AI-generated translation of the original Japanese article. For the authoritative version, please refer to the original. Read the original Japanese article → (https://quantumindex.jp/jij-sqoa-solver-qiskit-functions/) JIJ announced on November 5, 2026 that it has published the quantum optimization solver “SQOA Solver,” which implements the Sampling-based Quantum Optimization Algorithm (SQOA), in the Qiskit Functions Catalog on the IBM Quantum Platform. Using Quantum Relaxation to represent multiple binary variables with a single qubitqubitQubit / Quantum Bit / QubitThe basic unit of information in a quantum computer. It can represent not only 0 or 1 but also a quantum state that is a superposition of them.QI NoteHaving more qubits does not necessarily mean higher performance. Error rates, connectivity, coherence time, and the number of logical qubits are also important., the solver supports exploration of problems whose decision-variable count exceeds the number of available qubits. ✍️ Quantum Index Analysis The technical and commercial significance behind this announcement and evaluation points that are not obvious from headlines or numbers alone. Read our independent analysis ↓ Announcement overview SQOA Solver is provided in the Qiskit Functions Catalog under the name “jij/sqoa-solver.” It accepts binary combinatorial optimizationcombinatorial optimizationCombinatorial Optimization / Combinatorial OptimizationAn optimization problem that seeks, from many possible combinations of choices, the combination that satisfies given constraints while optimizing the objective value.QI NoteA representative candidate application of quantum computing, but classical algorithms are also very powerful. When evaluating quantum methods, comparisons under equivalent conditions are important. problems formulated as QUBO or Ising models and runs a unified workflow that covers classical preprocessing, problem encoding and quantum sampling via SQOA, solution extraction, and post‑processing improvement via local search. The quantum device is used for sampling, while classical computationclassical computationClassical Computing / Classical Computation / Classical Computing / Classical ComputationA computation method that uses bits of 0 and 1; the form of computation performed by the computers commonly used today.QI NoteUsed as a point of comparison with quantum computing, but it can encompass CPUs, GPUs, supercomputers, and specialized algorithms, so care should be taken about the conditions of comparison. is used to determine the parameters required for state preparation and to reconstruct and improve solutions. This architecture eliminates the need for iterative QPUQPUQuantum Processor / Quantum Processor / Quantum Processing Unit / QPUThe central part of the hardware that houses qubits and performs quantum computational operations such as quantum gates and measurements.QI NoteThe performance of a QPU cannot be judged by the number of qubits alone. Gate fidelity, connectivity, speed, error rates, and other factors must be considered together. executions aimed at tuning variational parameters. The underlying technique was proposed by JIJ authors Matsuyama and Yamashiro in the paper “Sampling-based Quantum Optimization Algorithm with Quantum Relaxation,” presented at QCE 2025. In August 2026, benchmarking evaluations were performed using MaxCut on 3-regular graphs and maximum independent set problems from QOBLIB. Key points • Published in the Qiskit Functions Catalog as “jij/sqoa-solver” • Uses Quantum Relaxation to represent multiple binary variables with a single qubit • Uses QPU for quantum sampling while handling parameter determination and solution reconstruction/improvement classically • Provides an integrated workflow from QUBO/Ising input through to local search • Enables researchers and developers to apply and test SQOA on their own optimization problems Technical and business implications Technically, the contribution lies in addressing qubit-count constraints by devising a variable-representation scheme, and in presenting an implementation that combines quantum sampling with classical pre- and post-processing. The design choice to avoid iterative variational-parameter optimization on the QPU could simplify execution on current hardware. From a business perspective, publishing on the Qiskit Functions Catalog broadens touchpoints for external users to try the tool. However, this announcement does not clarify performance advantages over classical methods or other quantum optimization approaches, nor does it specify terms of commercial use or the exact scope of the offering. What to watch next Going forward, results on solution quality and runtime as problem size increases, and comparisons with classical methods and other quantum optimization approaches, will be important. In addition to expanding benchmark coverage using resources like QOBLIB, external user evaluations on real-world problems and concrete usage examples will be key indicators. It will also be important to see whether usage terms, the service scope, and commercial-use policies are clarified. ✍️ Quantum Index Analysis The most notable aspect of this announcement is that JIJ has made an optimization algorithm designed to address current quantum-computer constraints—such as qubitqubitQubit / Quantum Bit / QubitThe basic unit of information in a quantum computer. It can represent not only 0 or 1 but also a quantum state that is a superposition of them.QI NoteHaving more qubits does not necessarily mean higher performance. Error rates, connectivity, coherence time, and the number of logical qubits are also important. counts and execution costs—available to external users as a service. SQOA maps multiple binary variables to a single qubit via Quantum Relaxation and combines quantum sampling with classical post-processing to handle problems with decision-variable counts exceeding the qubit budget. That said, SQOA should not be viewed as fundamentally different from QAOA-based quantum optimization in principle. SQOA assembles quantum statequantum stateQuantum State / Quantum StateRepresents the state of a quantum system and determines quantities such as the probabilities of outcomes obtained when the system is measured.QI NoteA quantum state is not the simple "0 or 1" value of a qubit. It includes superpositions and mixed states, and how the state was prepared and measured is also important. preparation, existing sampling, and classical post-processing techniques used in QAOA-like approaches, and its main distinguishing feature is the omission of per-problem variational-parameter optimization. Also, the ability to represent more variables than qubits does not automatically translate into overall speedup—one must evaluate the number of quantum sampling shots required and the classical computational cost for solution reconstruction and improvement. Moreover, SQOA is primarily an algorithm designed to work around current hardware limitations such as insufficient qubit counts and the cost of repeated QPUQPUQuantum Processor / Quantum Processor / Quantum Processing Unit / QPUThe central part of the hardware that houses qubits and performs quantum computational operations such as quantum gates and measurements.QI NoteThe performance of a QPU cannot be judged by the number of qubits alone. Gate fidelity, connectivity, speed, error rates, and other factors must be considered together. runs. It is unclear whether the same approach would remain competitive if fault-tolerant quantum computers (FTQCs) with ample logical qubitslogical qubitsLogical Qubit / Logical QubitA unit of information treated as a single qubit protected from errors by using multiple physical qubits and quantum error correction.QI NoteSimply having “created a logical qubit” does not necessarily mean fault-tolerant quantum computing (FTQC) has been achieved. One should verify logical error rates, operational/gate performance, and scalability. and deep circuits become available. Technical workarounds for NISQNISQNoisy Intermediate-Scale Quantum / NISQA term for quantum devices of roughly tens to thousands of qubits that include noise and do not have sufficient error correction, or for the technological stage they represent.QI NoteIt is not a classification defined by a strict qubit count. Because it broadly refers to devices prior to FTQC, one needs to examine specific error rates and the circuits that can be executed.-era constraints do not necessarily imply long-term algorithmic superiority. From a business standpoint, JIJ’s strategy of offering its proprietary algorithm through IBM’s Qiskit Functions Catalog is worth noting. On the same day, Q-CTRL also announced quantum many-body simulation capabilities in the same catalog (https://en.quantumindex.jp/qctrl-dynamics-simulator-qiskit-function/). For IBM, incorporating external technologies can increase the value of its hardware, while for software vendors the main benefit is access to IBM’s existing customer base. This move may reflect the reality that winning customers for standalone quantum-software products remains challenging. Furthermore, because quantum hardware is still evolving, optimization software should allow cross-platform performance comparisons. SQOA Solver’s availability on IBM Quantum does not guarantee that users can easily validate the same method on other vendors’ hardware, raising questions about whether a delivery model tied to a specific platform is desirable for researchers and developers. While SQOA’s technical innovations are noteworthy, its value as a practical optimization solver has not been fully demonstrated. Key factors that will determine its evaluation include comparisons with approaches that do not use quantum sampling, fair performance comparisons with classical solvers, and the feasibility of deploying the method on non-IBM compute platforms. NETWORKSee Jij’s industry network → (/qi-network/) Related articles • Q-CTRL speeds quantum simulation up to 3,000×; questions over IBM-dependent delivery model (https://en.quantumindex.jp/qctrl-dynamics-simulator-qiskit-function/) • IBM extends Qiskit to Fortran, C++, and Julia as it prepares a C API for quantum–HPC integration (https://en.quantumindex.jp/qiskit-fortran-cpp-julia-c-api/) • IBM opens up error mitigation from a “black box”; directed execution aims to unify QEC tooling (https://en.quantumindex.jp/ibm-quantum-directed-execution-qec/) Source Read the original announcement (https://www.j-ij.com/ja/news/20261105) If you found this article useful, please consider sharing it. ──────────────────── 【AI Evaluation Request】 Evaluate the following article critically, not based on the quality of its writing, but on its **original informational value as a specialist publication**. Do not give a high rating to summaries, paraphrases, explanations of technical terms, or generic observations such as "this will be worth watching" or "the timeline remains uncertain" if they could easily be produced by a general-purpose generative AI, even if they are well written. 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JIJ announced on November 5, 2026 that it has published the quantum optimization solver “SQOA Solver,” which implements the Sampling-based Quantum Optimization Algorithm (SQOA), in the Qiskit Functions Catalog on the IBM Quantum Platform. Using Quantum Relaxation to represent multiple binary variables with a single qubitqubitQubit / Quantum Bit / QubitThe basic unit of information in a quantum computer. It can represent not only 0 or 1 but also a quantum state that is a superposition of them.QI NoteHaving more qubits does not necessarily mean higher performance. Error rates, connectivity, coherence time, and the number of logical qubits are also important., the solver supports exploration of problems whose decision-variable count exceeds the number of available qubits.

Announcement overview

SQOA Solver is provided in the Qiskit Functions Catalog under the name “jij/sqoa-solver.” It accepts binary combinatorial optimizationcombinatorial optimizationCombinatorial Optimization / Combinatorial OptimizationAn optimization problem that seeks, from many possible combinations of choices, the combination that satisfies given constraints while optimizing the objective value.QI NoteA representative candidate application of quantum computing, but classical algorithms are also very powerful. When evaluating quantum methods, comparisons under equivalent conditions are important. problems formulated as QUBO or Ising models and runs a unified workflow that covers classical preprocessing, problem encoding and quantum sampling via SQOA, solution extraction, and post‑processing improvement via local search. The quantum device is used for sampling, while classical computationclassical computationClassical Computing / Classical Computation / Classical Computing / Classical ComputationA computation method that uses bits of 0 and 1; the form of computation performed by the computers commonly used today.QI NoteUsed as a point of comparison with quantum computing, but it can encompass CPUs, GPUs, supercomputers, and specialized algorithms, so care should be taken about the conditions of comparison. is used to determine the parameters required for state preparation and to reconstruct and improve solutions. This architecture eliminates the need for iterative QPUQPUQuantum Processor / Quantum Processor / Quantum Processing Unit / QPUThe central part of the hardware that houses qubits and performs quantum computational operations such as quantum gates and measurements.QI NoteThe performance of a QPU cannot be judged by the number of qubits alone. Gate fidelity, connectivity, speed, error rates, and other factors must be considered together. executions aimed at tuning variational parameters. The underlying technique was proposed by JIJ authors Matsuyama and Yamashiro in the paper “Sampling-based Quantum Optimization Algorithm with Quantum Relaxation,” presented at QCE 2025. In August 2026, benchmarking evaluations were performed using MaxCut on 3-regular graphs and maximum independent set problems from QOBLIB.

Key points

• Published in the Qiskit Functions Catalog as “jij/sqoa-solver”
• Uses Quantum Relaxation to represent multiple binary variables with a single qubit
• Uses QPU for quantum sampling while handling parameter determination and solution reconstruction/improvement classically
• Provides an integrated workflow from QUBO/Ising input through to local search
• Enables researchers and developers to apply and test SQOA on their own optimization problems

Technical and business implications

Technically, the contribution lies in addressing qubit-count constraints by devising a variable-representation scheme, and in presenting an implementation that combines quantum sampling with classical pre- and post-processing. The design choice to avoid iterative variational-parameter optimization on the QPU could simplify execution on current hardware. From a business perspective, publishing on the Qiskit Functions Catalog broadens touchpoints for external users to try the tool. However, this announcement does not clarify performance advantages over classical methods or other quantum optimization approaches, nor does it specify terms of commercial use or the exact scope of the offering.

What to watch next

Going forward, results on solution quality and runtime as problem size increases, and comparisons with classical methods and other quantum optimization approaches, will be important. In addition to expanding benchmark coverage using resources like QOBLIB, external user evaluations on real-world problems and concrete usage examples will be key indicators. It will also be important to see whether usage terms, the service scope, and commercial-use policies are clarified.

✍️ Quantum Index Analysis

The most notable aspect of this announcement is that JIJ has made an optimization algorithm designed to address current quantum-computer constraints—such as qubitqubitQubit / Quantum Bit / QubitThe basic unit of information in a quantum computer. It can represent not only 0 or 1 but also a quantum state that is a superposition of them.QI NoteHaving more qubits does not necessarily mean higher performance. Error rates, connectivity, coherence time, and the number of logical qubits are also important. counts and execution costs—available to external users as a service. SQOA maps multiple binary variables to a single qubit via Quantum Relaxation and combines quantum sampling with classical post-processing to handle problems with decision-variable counts exceeding the qubit budget.

That said, SQOA should not be viewed as fundamentally different from QAOA-based quantum optimization in principle. SQOA assembles quantum statequantum stateQuantum State / Quantum StateRepresents the state of a quantum system and determines quantities such as the probabilities of outcomes obtained when the system is measured.QI NoteA quantum state is not the simple "0 or 1" value of a qubit. It includes superpositions and mixed states, and how the state was prepared and measured is also important. preparation, existing sampling, and classical post-processing techniques used in QAOA-like approaches, and its main distinguishing feature is the omission of per-problem variational-parameter optimization. Also, the ability to represent more variables than qubits does not automatically translate into overall speedup—one must evaluate the number of quantum sampling shots required and the classical computational cost for solution reconstruction and improvement.

Moreover, SQOA is primarily an algorithm designed to work around current hardware limitations such as insufficient qubit counts and the cost of repeated QPUQPUQuantum Processor / Quantum Processor / Quantum Processing Unit / QPUThe central part of the hardware that houses qubits and performs quantum computational operations such as quantum gates and measurements.QI NoteThe performance of a QPU cannot be judged by the number of qubits alone. Gate fidelity, connectivity, speed, error rates, and other factors must be considered together. runs. It is unclear whether the same approach would remain competitive if fault-tolerant quantum computers (FTQCs) with ample logical qubitslogical qubitsLogical Qubit / Logical QubitA unit of information treated as a single qubit protected from errors by using multiple physical qubits and quantum error correction.QI NoteSimply having “created a logical qubit” does not necessarily mean fault-tolerant quantum computing (FTQC) has been achieved. One should verify logical error rates, operational/gate performance, and scalability. and deep circuits become available. Technical workarounds for NISQNISQNoisy Intermediate-Scale Quantum / NISQA term for quantum devices of roughly tens to thousands of qubits that include noise and do not have sufficient error correction, or for the technological stage they represent.QI NoteIt is not a classification defined by a strict qubit count. Because it broadly refers to devices prior to FTQC, one needs to examine specific error rates and the circuits that can be executed.-era constraints do not necessarily imply long-term algorithmic superiority.

From a business standpoint, JIJ’s strategy of offering its proprietary algorithm through IBM’s Qiskit Functions Catalog is worth noting. On the same day, Q-CTRL also announced quantum many-body simulation capabilities in the same catalog. For IBM, incorporating external technologies can increase the value of its hardware, while for software vendors the main benefit is access to IBM’s existing customer base. This move may reflect the reality that winning customers for standalone quantum-software products remains challenging.

Furthermore, because quantum hardware is still evolving, optimization software should allow cross-platform performance comparisons. SQOA Solver’s availability on IBM Quantum does not guarantee that users can easily validate the same method on other vendors’ hardware, raising questions about whether a delivery model tied to a specific platform is desirable for researchers and developers.

While SQOA’s technical innovations are noteworthy, its value as a practical optimization solver has not been fully demonstrated. Key factors that will determine its evaluation include comparisons with approaches that do not use quantum sampling, fair performance comparisons with classical solvers, and the feasibility of deploying the method on non-IBM compute platforms.

NETWORKSee Jij’s industry network →

Related articles

• Q-CTRL speeds quantum simulation up to 3,000×; questions over IBM-dependent delivery model
• IBM extends Qiskit to Fortran, C++, and Julia as it prepares a C API for quantum–HPC integration
• IBM opens up error mitigation from a “black box”; directed execution aims to unify QEC tooling

Source

Read the original announcement