This work constitutes a reproducible baseline for noise-aware quantum machine learning on pharmaceutical data and shows that hardware-efficient entanglement structures—specifically alternating cost and mixer layers—provide a systematic advantage over purely rotational or diagonal encodings under realistic noise conditions.
Abstract
We present the first systematic, hardware-executed benchmark of twelve distinct quantum data-encoding strategies for drug-response prediction on a real superconducting quantum processing unit (QPU). All experiments were conducted on the IQM Garnet 20-qubit QPU via the IQM Resonance cloud platform, using the Qrisp quantum-software framework (v 0.8.2). Each encoding was evaluated on n = 50 stratified samples drawn from the Genomics of Drug Sensitivity in Cancer dataset (GDSC2, 242 036 drug–cell-line pairs), targeting the natural-log IC50 response variable. Variational weights were optimised offline with the gradient-free COBYLA algorithm before hardware submission. Every circuit was executed with 1024 shots; the regression signal is the zero-qubit Pauli expectation value ⟨Z0⟩. Results show that the QAOA-inspired encoding achieves the best RMSE of 3.314 and is statistically superior (p < 0.05, Wilcoxon signed-rank test) to six of the remaining eleven encodings. Hardware-efficient entanglement structures—specifically alternating cost and mixer layers—provide a systematic advantage over purely rotational or diagonal encodings under realistic noise conditions. This work constitutes a reproducible baseline for noise-aware quantum machine learning on pharmaceutical data; all code, data, and raw QPU outputs are publicly released.
Quantum machine learning (QML) faces practical limitations due to noisy intermediate-scale quantum (NISQ) constraints, including noise, restricted qubit availability, and unstable optimization. This paper proposes HyQNet, a resource-aware hybrid quantum–classical framework designed to address these challenges through efficient circuit execution and adaptive optimization. The framework integrates optimized quantum circuits with classical learning strategies to improve scalability and stability under NISQ conditions. Experimental results on Iris, Wine, and Breast Cancer datasets show that HyQNet achieves an accuracy of 95.1% and F1-score of 94.8%, outperforming variational QNN (92.6%) and quantum SVM (91.2%). It also reduces runtime to 16.9 s compared to 20.5 s for VQNN, while maintaining efficient utilization of 8 qubits. Statistical analysis confirms significance (p < 0.05), and ablation studies validate the contribution of each component. The results demonstrate improved convergence stability and resource efficiency in hybrid quantum learning systems.
Sudheer Reddy K., Hastimal Jangid, Usha Desai· 2026 International Conferenc...· 0 citations
This work introduces Quantum Convolutional HLA Immunogenic Peptide Prediction (Q-CHIPP), a combinatorial framework integrating MHC binding and T-cell recognition that targets HLA-A*02:01–restricted 9-mer peptides and identifies those peptides known to be immunogenic, improving the prognostic impact of predicted neoantigen load.
Ryan Peters, Kahn Rhrissorrakrai, Prerana Bangalore Parthasarathy et al.· Science Advances· 0 citations
This study systematically evaluates the efficacy of two quantum machine learning algorithms-Quantum Support Vector Machine (QSVM) and Quantum Neural Networks (QNN) on IBM quantum simulation platforms to provide a comprehensive assessment of QSVM and QNN under current Noisy Intermediate-Scale Quantum (NISQ) constraints.
Despite rapid progress in quantum optimization, the field lacks broad real-hardware benchmarks comparing multiple algorithmic families across diverse classically hard combinatorial problems under one protocol. We present a hardware-aware benchmark of gate-based quantum optimization across four NP-hard problems: the multi-dimensional knapsack problem (MDKP), maximum independent set (MIS), quadratic assignment problem (QAP), and market-share problem, spanning variational methods (VQE, CVaR-VQE), standard, multi-angle, and warm-start QAOA, and qubit-efficient encodings (Pauli correlation encoding (PCE), QRAO), executed on IBM Heron r1/r2 processors under resilience-level-2 mitigation. To our knowledge this includes the first real-hardware QRAO results and the first multi-problem PCE hardware benchmark. Across 247 method–instance combinations we report transpiled circuit size, an independent-error gate-count fidelity proxy Fest, and hardware outcomes. An empirical operating point near Fest≈0.1 ( ∼770 two-qubit gates at the median Heron-r2 CZ error rate) marks the transition to noise-dominated execution in the MDKP and MIS regimes. Two limitations emerge. QAP couples dense one-hot encodings with an exponentially sparse feasible manifold (feasible fraction 10!/2100 at n=10); no tested hardware method returns a feasible assignment. The tested QAOA-family circuits become noise-dominated after compilation, and a matched uniform-random control shows most feasible low-fidelity outcomes lie within the random range, with one MIS warm-start result reported as a finite-sample exception. A compilation counterfactual (SWAP-aware, fractional-gate, Nighthawk-topology) reduces two-qubit counts but moves no circuit above Fest=10−3; conclusions therefore apply to the tested implementations, not QAOA in general. Qubit-efficient methods extend runnable instance sizes but gain only within the empirical fidelity budget33 Code available at: https://github.com/SMU-Quantum/quantum-optimization-benchmarks.. Code available at: https://github.com/SMU-Quantum/quantum-optimization-benchmarks.
Monit Sharma, HoongChuin Lau· Quantum Science and Technolo...· 1 citation
This thesis studies exact, deterministic preparation of arbitrary dense n-qubit states, the data-loading step in quantum signal and image processing. It derives two syntheses built on the Digital Signal-induced Heap Transform (DsiHT): the QsiHT Fast Path Real Synthesis and the QsiHT Fast Path Complex Synthesis. Both are benchmarked against ten configurations spanning the UCR, isometry, multiplexor, Schmidt/SVD, QSD, and heap-transform families. Several of those are realizations through Qiskit builders or compiler optimization, not from-scratch reimplementations. The n=3 noisy comparison spans ibm_fez, ibm_kingston, and ibm_marrakesh, three 156-qubit IBM Heron r2 processors, where single-submission nine-method jobs permit within-session family-wide Benjamini-Hochberg-corrected comparisons. The frontier-versus-QSD separation reproduces within one calibration on every device, but the within-frontier order does not reproduce across devices or calibration days. A deep-circuit n=8 run on ibm_fez shows the executed-count ordering re-emerge outside the run-to-run spread on the complex target. Depth was not isolated from mapping, gate composition, calibration, or session effects. All methods are exact to machine precision and differ only in cost. Under noise the coarse error tier tracks the executed (routed) two-qubit count, separating the Theta(2^n) frontier from QSD's Theta(4^n) and nothing finer. Both syntheses realize the deployed Qiskit StatePreparation floor of 2^n-n-1 CNOTs, undercutting every other from-scratch method on the as-built CNOT axis. The Real Synthesis serves real (sign-bearing) targets and holds the lowest classical build cost among the exact loaders at large register sizes, through one fast Walsh-Hadamard pass. The Complex Synthesis serves arbitrary complex targets and ties Qiskit's StatePreparation for the lowest simulated sampled error.
This work reveals and exploits this underexplored robustness property: how much non-Clifford and variational expressivity can be removed from the sampling circuit before SQD accuracy degrades, and answers through two complementary compression techniques: gradient-based operator pruning, which discards low-impact excitation operators, and Clifford rounding, which snaps remaining parameters to the nearest Clifford angle.
Kangyu Zheng, Yidong Zhou, Jinglei Cheng et al.· 0 citations