Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

The State Measurement Engine: Validating and Applying a Quantum Projector

P. K. Majumdar

Published Aug 28, 2026• 0 citations
Publisher

Abstract

This Python/NumPy program implements an ideal projective measurement for a single-qubit state. It verifies that the supplied state is normalised and that the measurement operator is both Hermitian and idempotent. It then applies the Born rule to calculate the probability of the selected outcome. When the probability is non-zero, the program constructs and normalises the conditional post-measurement state; when the probability is zero within the specified numerical tolerance, it reports that the outcome cannot occur and avoids invalid normalisation of the zero vector. Two worked examples demonstrate both a possible and an impossible measurement outcome.

DOI
10.5281/zenodo.22134716

Related papers

Constructing and Validating Quantum Projection Operators with NumPy

P. K. Majumdar

This program constructs a projection operator from a normalised complex state vector and verifies its Hermiticity, idempotence, trace and eigenvalues. It also generates the complementary projector, confirms that the two outcome projectors resolve the identity, and calculates measurement probabilities for an example input state. Numerical tolerances are used to accommodate floating-point arithmetic.

•Aug 25, 2026•0 citations

Qubit measurement probabilities with the Born rule

P. K. Majumdar

This Jupyter Notebook provides a reproducible numerical demonstration of the Born rule for projective measurements of a single qubit. It explains how a complex inner product is converted into a physically meaningful measurement probability. Using Python and NumPy, the notebook defines a validated function that calculates the probability amplitude between an initial quantum state and a measurement state. It then calculates the transition probability by taking the squared absolute value of that amplitude. The implementation checks that both state vectors are finite, one dimensional, equal in size and correctly normalised. It uses the NumPy vdot function to apply complex conjugation to the measurement state correctly. In the worked example, the qubit is prepared in the computational zero state and measured in the Pauli X basis. The calculation shows that the probabilities of obtaining the plus and minus outcomes are both 0.5. It also confirms that the two probabilities sum to 1. The numerical result is compared with the corresponding Bloch sphere prediction. The notebook verifies that the vector calculation and the geometric calculation both produce a transition probability of 0.5. Numerical consistency checks confirm agreement between the two calculation methods and verify probability conservation. The notebook requires Python 3, Jupyter Notebook or JupyterLab, and NumPy. It does not require a quantum computing framework or access to physical quantum hardware.

•Aug 11, 2026•0 citations

Simulating qubit measurement probabilities with the Born rule

P. K. Majumdar

This Jupyter Notebook provides a reproducible simulation of single qubit measurement probabilities using the Born rule. It accompanies Chapter 2 of Quantum Computing Preliminaries and demonstrates the relationship between complex vector calculations, Bloch sphere geometry and experimentally observed measurement frequencies. The notebook implements a validated Python function that calculates both the complex probability amplitude and the corresponding transition probability for an initial quantum state and a measurement state. It checks that the supplied vectors are finite, one dimensional, equal in size and correctly normalised. Invalid or physically inconsistent inputs produce informative errors. A worked example prepares a qubit in the computational zero state and measures it in the Pauli X basis. The calculated probabilities of obtaining the plus and minus outcomes are both 0.5, and their sum is verified to be 1. The result is independently confirmed using the geometric relationship between the corresponding Bloch sphere vectors. The notebook also constructs general pure qubit states at different polar and azimuthal angles. It compares numerical transition probabilities with the Bloch sphere half angle prediction across a range of sampled angles and verifies agreement within a numerical tolerance. A finite measurement simulation using 10000 trials illustrates the distinction between an exact theoretical probability and an observed experimental frequency. A fixed random seed of 2026 ensures that this demonstration is reproducible. The notebook records the Python version, NumPy version and execution platform. It requires only Python 3, Jupyter Notebook or JupyterLab, and NumPy. No external datasets, internet connection, quantum computing framework or physical quantum hardware are required.

•Aug 11, 2026•0 citations

The Orthogonality Filter: Gram–Schmidt Projection in a Complex State Space

P. K. Majumdar

This Python/NumPy program generates a reproducible, normalised random quantum state and constructs its rank-one projection operator. It then generates a second complex state and applies the Gram–Schmidt procedure to remove every component parallel to the first state, producing a normalised orthogonal vector. The program verifies the inner-product orthogonality, confirms that the projector is Hermitian and idempotent, and demonstrates that applying the projector to the orthogonal state produces a vector whose norm is numerically zero within a declared floating-point tolerance. This illustrates, computationally, how a projector completely filters out a state that lies in its orthogonal complement.

•Aug 28, 2026•0 citations

Simulation of Single Qubit Measurement Probabilities in Computational and Hadamard Bases

P. K. Majumdar

This Jupyter Notebook provides a reproducible classical simulation of measurement probabilities for a single qubit using the Born rule The notebook accompanies Chapter 2 of Quantum Computing Preliminaries and demonstrates how the same quantum state produces different probability distributions when measured in different orthonormal bases A generic pure qubit state is constructed from a polar angle of pi divided by 3 and an azimuthal angle of pi divided by 4 on the Bloch sphere The notebook calculates the probabilities of measuring this state in the computational basis and in the Hadamard basis In the computational basis the probabilities of the zero and one outcomes are three quarters and one quarter In the Hadamard basis the probabilities of the plus and minus outcomes are calculated from the corresponding complex inner products The implementation validates that the quantum state is finite one dimensional and normalised It also verifies that each supplied measurement basis has the correct dimensions and is orthonormal Numerical consistency checks confirm that the probabilities obtained in each basis independently sum to 1 within a suitable floating point tolerance The notebook uses Python 3 and NumPy No external datasets internet connection quantum computing framework or physical quantum hardware are required

•Aug 11, 2026•0 citations

A non-probabilistic substitute for the Born rule

Daniela Frauchiger, Renato Renner

The Born rule assigns a probability to any possible outcome of a quantum measurement, but leaves open the question how these probabilities are to be interpreted and, in particular, how they relate to the outcome observed in an actual experiment. We propose to avoid this question by replacing the Born rule with two non-probabilistic postulates: (i) the projector associated to the observed outcome must have a positive overlap with the state of the measured system; (ii) statements about observed outcomes are robust, that is, remain valid under small perturbations of the state. We show that the two postulates suffice to retrieve the interpretations of the Born rule that are commonly used for analysing experimental data.

arXiv:1710.05033•Oct 13, 2017•2 citations
This Python/NumPy program implements an ideal projective measurement for a single-qubit state. It verifies that the supplied state is normalised and that the measurement operator is both Hermitian and idempotent. It then applies the Born rule to calculate the probability of the selected outcome. When the probability is non-zero, the program constructs and normalises the conditional post-measurement state; when the probability is zero within the specified numerical tolerance, it reports that the outcome cannot occur and avoids invalid normalisation of the zero vector. Two worked examples demonstrate both a possible and an impossible measurement outcome.
This Jupyter Notebook provides a reproducible numerical demonstration of the Born rule for projective measurements of a single qubit. It explains how a complex inner product is converted into a physically meaningful measurement probability. Using Python and NumPy, the notebook defines a validated function that calculates the probability amplitude between an initial quantum state and a measurement state. It then calculates the transition probability by taking the squared absolute value of that amplitude. The implementation checks that both state vectors are finite, one dimensional, equal in size and correctly normalised. It uses the NumPy vdot function to apply complex conjugation to the measurement state correctly. In the worked example, the qubit is prepared in the computational zero state and measured in the Pauli X basis. The calculation shows that the probabilities of obtaining the plus and minus outcomes are both 0.5. It also confirms that the two probabilities sum to 1. The numerical result is compared with the corresponding Bloch sphere prediction. The notebook verifies that the vector calculation and the geometric calculation both produce a transition probability of 0.5. Numerical consistency checks confirm agreement between the two calculation methods and verify probability conservation. The notebook requires Python 3, Jupyter Notebook or JupyterLab, and NumPy. It does not require a quantum computing framework or access to physical quantum hardware.
This Jupyter Notebook provides a reproducible simulation of single qubit measurement probabilities using the Born rule. It accompanies Chapter 2 of Quantum Computing Preliminaries and demonstrates the relationship between complex vector calculations, Bloch sphere geometry and experimentally observed measurement frequencies. The notebook implements a validated Python function that calculates both the complex probability amplitude and the corresponding transition probability for an initial quantum state and a measurement state. It checks that the supplied vectors are finite, one dimensional, equal in size and correctly normalised. Invalid or physically inconsistent inputs produce informative errors. A worked example prepares a qubit in the computational zero state and measures it in the Pauli X basis. The calculated probabilities of obtaining the plus and minus outcomes are both 0.5, and their sum is verified to be 1. The result is independently confirmed using the geometric relationship between the corresponding Bloch sphere vectors. The notebook also constructs general pure qubit states at different polar and azimuthal angles. It compares numerical transition probabilities with the Bloch sphere half angle prediction across a range of sampled angles and verifies agreement within a numerical tolerance. A finite measurement simulation using 10000 trials illustrates the distinction between an exact theoretical probability and an observed experimental frequency. A fixed random seed of 2026 ensures that this demonstration is reproducible. The notebook records the Python version, NumPy version and execution platform. It requires only Python 3, Jupyter Notebook or JupyterLab, and NumPy. No external datasets, internet connection, quantum computing framework or physical quantum hardware are required.
This Python/NumPy program generates a reproducible, normalised random quantum state and constructs its rank-one projection operator. It then generates a second complex state and applies the Gram–Schmidt procedure to remove every component parallel to the first state, producing a normalised orthogonal vector. The program verifies the inner-product orthogonality, confirms that the projector is Hermitian and idempotent, and demonstrates that applying the projector to the orthogonal state produces a vector whose norm is numerically zero within a declared floating-point tolerance. This illustrates, computationally, how a projector completely filters out a state that lies in its orthogonal complement.
This Jupyter Notebook provides a reproducible classical simulation of measurement probabilities for a single qubit using the Born rule The notebook accompanies Chapter 2 of Quantum Computing Preliminaries and demonstrates how the same quantum state produces different probability distributions when measured in different orthonormal bases A generic pure qubit state is constructed from a polar angle of pi divided by 3 and an azimuthal angle of pi divided by 4 on the Bloch sphere The notebook calculates the probabilities of measuring this state in the computational basis and in the Hadamard basis In the computational basis the probabilities of the zero and one outcomes are three quarters and one quarter In the Hadamard basis the probabilities of the plus and minus outcomes are calculated from the corresponding complex inner products The implementation validates that the quantum state is finite one dimensional and normalised It also verifies that each supplied measurement basis has the correct dimensions and is orthonormal Numerical consistency checks confirm that the probabilities obtained in each basis independently sum to 1 within a suitable floating point tolerance The notebook uses Python 3 and NumPy No external datasets internet connection quantum computing framework or physical quantum hardware are required
The Born rule assigns a probability to any possible outcome of a quantum measurement, but leaves open the question how these probabilities are to be interpreted and, in particular, how they relate to the outcome observed in an actual experiment. We propose to avoid this question by replacing the Born rule with two non-probabilistic postulates: (i) the projector associated to the observed outcome must have a positive overlap with the state of the measured system; (ii) statements about observed outcomes are robust, that is, remain valid under small perturbations of the state. We show that the two postulates suffice to retrieve the interpretations of the Born rule that are commonly used for analysing experimental data.