Narges Alavisamani

Ramin Ayanzadeh, Narges Alavisamani, Poulami Das et al.

Quantum Approximate Optimization Algorithm (QAOA) is one of the leading candidates for demonstrating the quantum advantage using near-term quantum computers. Unfortunately, high device error rates limit us from reliably running QAOA circuits for problems with more than a few qubits. In QAOA, the problem graph is translated into a quantum circuit such that every edge corresponds to two 2-qubit CNOT operations in each layer of the circuit. As CNOTs are extremely error-prone, the fidelity of QAOA circuits is dictated by the number of edges in the problem graph. We observe that majority of graphs corresponding to real-world applications follow the ``power-law`` distribution, where some hotspot nodes have significantly higher number of connections. We leverage this insight and propose ``FrozenQubits`` that freezes the hotspot nodes or qubits and intelligently partitions the state-space of the given problem into several smaller sub-spaces which are then solved independently. The corresponding QAOA sub-circuits are significantly less vulnerable to gate and decoherence errors due to the reduced number of CNOT operations in each sub-circuit. Unlike prior circuit-cutting approaches, FrozenQubits does not require any exponentially complex post-processing step. Our evaluations with 5,300 QAOA circuits on eight different quantum computers from IBM shows that FrozenQubits can improve the quality of solutions by 8.73x on average (and by up to 57x), albeit utilizing 2x more quantum resources.

Ramin Ayanzadeh, Ahmad Mousavi, Amirhossein Basareh et al.

The Early Fault-Tolerant (EFT) era is emerging, where modest Quantum Error Correction (QEC) can enable quantum utility before full-scale fault tolerance. Quantum optimization is a leading candidate for early applications, but protecting these workloads is critical since they will run on expensive cloud services where providers could learn sensitive problem details. Experience with classical computing systems has shown that treating security as an afterthought can lead to significant vulnerabilities. Thus, we must address the security implications of quantum computing before widespread adoption. However, current Secure Quantum Computing (SQC) approaches, although theoretically promising, are impractical in the EFT era: blind quantum computing requires large-scale quantum networks, and quantum homomorphic encryption depends on full QEC. We propose application-specific SQC, a principle that applies obfuscation at the application layer to enable practical deployment while remaining agnostic to algorithms, computing models, and hardware architectures. We present Enigma, the first realization of this principle for quantum optimization. Enigma integrates three complementary obfuscations: ValueGuard scrambles coefficients, StructureCamouflage inserts decoys, and TopologyTrimmer prunes variables. These techniques guarantee recovery of original solutions, and their stochastic nature resists repository-matching attacks. Evaluated against seven state-of-the-art AI models across five representative graph families, even combined adversaries, under a conservatively strong attacker model, identify the correct problem within their top five guesses in only 4.4% of cases. The protections come at the cost of problem size and T-gate counts increasing by averages of 1.07x and 1.13x, respectively, with both obfuscation and decoding completing within seconds for large-scale problems.

Sashwat Anagolum, Narges Alavisamani, Poulami Das et al.

Designing performant and noise-robust circuits for Quantum Machine Learning (QML) is challenging -- the design space scales exponentially with circuit size, and there are few well-supported guiding principles for QML circuit design. Although recent Quantum Circuit Search (QCS) methods attempt to search for performant QML circuits that are also robust to hardware noise, they directly adopt designs from classical Neural Architecture Search (NAS) that are misaligned with the unique constraints of quantum hardware, resulting in high search overheads and severe performance bottlenecks. We present Élivágar, a novel resource-efficient, noise-guided QCS framework. Élivágar innovates in all three major aspects of QCS -- search space, search algorithm and candidate evaluation strategy -- to address the design flaws in current classically-inspired QCS methods. Élivágar achieves hardware-efficiency and avoids an expensive circuit-mapping co-search via noise- and device topology-aware candidate generation. By introducing two cheap-to-compute predictors, Clifford noise resilience and Representational capacity, Élivágar decouples the evaluation of noise robustness and performance, enabling early rejection of low-fidelity circuits and reducing circuit evaluation costs. Due to its resource-efficiency, Élivágar can further search for data embeddings, significantly improving performance. Based on a comprehensive evaluation of Élivágar on 12 real quantum devices and 9 QML applications, Élivágar achieves 5.3% higher accuracy and a 271$\times$ speedup compared to state-of-the-art QCS methods.