Persistent hardware noise in quantum computing

The gap

Evidence profile

Sourced from the stated challenges and stated research gap and limitations section and future-work section of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 8 representative gaps

• ECCentric: An Empirical Analysis of Quantum Error Correction Codes (2026) · doi

  Persistent hardware noise in quantum computing. Limited understanding of QEC code effectiveness under varying conditions. Difficulty in comparing decoder performance across QEC code families. Limited scalability of quantum computing systems due to noise.

  generalstated challengesevidence 5/5
  Keywords: persistent hardware noise quantum computing limited understanding qec
• Quantum Computing: Principles, Evolution, Applications, and Future Prospects (2026) · International Journal of Science Strategic Management and Technology · doi

  Classical computers are inefficient for simulating quantum systems. There is a need for the development of quantum machines for simulating quantum systems. The paper identifies the need for advancements in quantum error correction, hardware design, and algorithm optimization.

  generalstated research gapevidence 5/5
  Keywords: classical computers inefficient simulating quantum systems there need
• Quantum Computing: Principles, Evolution, Applications, and Future Prospects (2026) · International Journal of Science Strategic Management and Technology · doi

  Current quantum computers contain a limited number of qubits. Scaling up quantum systems while maintaining coherence and minimizing noise is a major engineering challenge. Quantum computers require highly specialized hardware environments. Increasing qubit count often leads to higher error rates and system complexity.

  generallimitations sectionevidence 5/5
  Keywords: current quantum computers contain limited number qubits scaling
• Noise-induced Simulability Transition from Operator Scrambling (2026) · arXiv

  To further understand the role of noise in determining the complexity of simulating quantum systems. To develop quantum algorithms and protocols that are robust to noise. To explore the implications of the noise-induced simulability transition for quantum computing and quantum information processing.

  generalfuture-work sectionevidence 5/5
  Keywords: further understand role noise determining complexity simulating quantum
• Noise-induced Simulability Transition from Operator Scrambling (2026) · arXiv

  The development of quantum algorithms and protocols that are robust to noise. The understanding of the role of noise in determining the complexity of simulating quantum systems. The identification of the critical noise level per cycle below which classical simulation remains exponentially hard.

  generalstated challengesevidence 5/5
  Keywords: development quantum algorithms protocols robust noise understanding role
• Sequential state preparation and measurement of multiple qubits via a single channel (2026) · npj Quantum Information · doi

  The hardware overhead needed for simultaneous state preparation and measurement of millions of qubits represents one of many challenges that must be overcome when scaling up quantum processors. The paper identifies a gap in the existing solutions, which typically use individual readout hardware for each qubit, presenting a significant overhead.

  generalstated research gapevidence 5/5
  Keywords: hardware overhead needed simultaneous state preparation measurement millions
• Sequential state preparation and measurement of multiple qubits via a single channel (2026) · npj Quantum Information · doi

  The paper identifies the challenge of minimising data qubit flips to enable high-fidelity SPAM. The paper notes the challenge of achieving high-fidelity SPAM with poor single-shot readout. The paper highlights the challenge of scaling up quantum processors due to the hardware overhead needed for simultaneous state preparation and measurement.

  generalstated challengesevidence 5/5
  Keywords: paper identifies challenge minimising data qubit flips enable
• Digitized counterdiabatic quantum feature extraction (2026) · Scientific Reports · doi

  High-dimensional, noisy, or redundant data can make it difficult for ML algorithms to directly identify meaningful patterns. Capturing statistical dependencies that are difficult to access with standard classical methods is a challenge. The authors face the challenge of executing quantum circuits on a noisy quantum processor with limited qubits and shots per circuit.

  generalstated challengesevidence 5/5
  Keywords: high-dimensional noisy redundant data make difficult algorithms directly

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