{
  "patent_number": "US 20240296359",
  "country": "US",
  "title": "How Quantum Computers Learn to Sort Data",
  "original_title": "Classification using quantum neural networks",
  "summary": "This patent describes a method for training a data classifier using a quantum computer, where classical AI helps prepare the data and interpret the quantum results to improve the quantum system's ability to sort information.",
  "what_it_does": "The patent details a method for training a classifier that runs on a quantum computer. First, a classical artificial neural network prepares a set of quantum bits (qubits) in an input state with known categories (Claim 1). These qubits then go through a series of \"parameterized quantum gates\" — like adjustable switches — which change their quantum state. Another classical artificial neural network then reads the final state of specific \"readout qubits\" to guess the category of the initial data. This guess is compared to the known category, and the quantum gates' adjustable settings (parameters) are tweaked to make future guesses more accurate (Claim 1, 5). This training process repeats until the classifier performs well (Claim 2). Once trained, the quantum computer can then classify new, unknown data by applying the learned quantum gates and interpreting the readout with the second classical network (Claim 15). For example, a quantum computer could be trained to distinguish between quantum states representing different types of particles, with classical AI assisting in the setup and interpretation.",
  "what_it_does_not_cover": [
    "Training a quantum classifier without using any classical artificial neural networks for preparing the input state or determining the predicted classification (Claim 1).",
    "Classification methods that do not involve parameterized quantum gates (Claim 1).",
    "Quantum machine learning tasks other than classification, such as regression or generative models.",
    "Training methods that update parameters of a classical neural network based on quantum computations, rather than updating parameters of quantum gates.",
    "Classification systems where the input state is prepared purely classically without being encoded into qubits."
  ],
  "filed": "2024-04-26",
  "granted": null,
  "expires": "2044-04-26",
  "status": "active",
  "holder": "Google",
  "holder_url": "https://patentbrief.org/company/google",
  "inventors": [
    {
      "name": "Edward Henry Farhi",
      "url": "https://patentbrief.org/inventor/edward-henry-farhi"
    },
    {
      "name": "Hartmut Neven",
      "url": "https://patentbrief.org/inventor/hartmut-neven"
    }
  ],
  "times_cited": 0,
  "tags": [
    "quantum_computing",
    "ai_ml",
    "software",
    "telecommunications",
    "semiconductors",
    "biotech"
  ],
  "abstract": "This disclosure relates to classification methods that can be implemented on quantum computing systems. According to a first aspect, this specification describes a method for training a classifier implemented on a quantum computer, the method comprising: preparing a plurality of qubits in an input state with a known classification, said plurality of qubits comprising one or more readout qubits; applying one or more parameterised quantum gates to the plurality of qubits to transform the input state to an output state; determining, using a readout state of the one or more readout qubits in the output state, a predicted classification of the input state; comparing the predicted classification with the known classification; and updating one or more parameters of the parameterised quantum gates in dependence on the comparison of the predicted classification with the known classification.",
  "url": "https://patentbrief.org/patent/us/20240296359/classification-using-quantum-neural-networks",
  "markdown_url": "https://patentbrief.org/patent/us/20240296359/classification-using-quantum-neural-networks/md",
  "google_patents_url": "https://patents.google.com/patent/US20240296359",
  "relatedPatents": [
    {
      "patentNumber": "9256834",
      "countryCode": "US",
      "title": "How Quantum Computers Use Special Devices to Control Qubits",
      "url": "https://patentbrief.org/patent/us/9256834/quantum-computers-having-partial-interferometric-quantum-gates"
    },
    {
      "patentNumber": "11295225",
      "countryCode": "US",
      "title": "How D-Wave Clears Magnetic Noise in Quantum Computers",
      "url": "https://patentbrief.org/patent/us/11295225/superconducting-quantum-processor-and-method-of-operating-same"
    },
    {
      "patentNumber": "11170293",
      "countryCode": "US",
      "title": "How AI Systems Learn to Predict and Act Simultaneously",
      "url": "https://patentbrief.org/patent/us/11170293/alphago-policy-and-value-networks"
    },
    {
      "patentNumber": "11544573",
      "countryCode": "US",
      "title": "How Projection Neural Networks Speed Up AI Predictions",
      "url": "https://patentbrief.org/patent/us/11544573/llama-large-language-model-architecture"
    },
    {
      "patentNumber": "12423586",
      "countryCode": "US",
      "title": "Making AI Smarter by Focusing on Unsure 'Nodes'",
      "url": "https://patentbrief.org/patent/us/12423586/training-nodes-of-a-neural-network-to-be-decisive"
    }
  ]
}