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building a quantum random number generator skip to main content quantumcomputing courses com courses all courses course platforms coursera edx udemy brilliant hardware providers google quantum ai ibm quantum ionq quantinuum amazon braket azure quantum quera rigetti d wave tutorials all tutorials hello world qiskit hello world cirq hello world pennylane hello world braket quantum gates grover s algorithm shor s algorithm reference all frameworks qiskit cirq pennylane amazon braket pyquil tket d wave ocean q explore learn learning paths prerequisites programming guide case studies glossary books quantum news podcasts tools bloch sphere quantum pinball guides algorithm guide hardware guide qubit types framework comparison migration guide language timeline cheat sheets career events 2026 jobs careers certifications salary guide universities interview prep faq troubleshooting about company editorial policy search browse courses home tutorials building a quantum random number generator qiskit beginner free 50 61 in series 10 min read 24 mar 2026 editorial policy building a quantum random number generator build a true quantum random number generator using superposition and measurement in qiskit and understand why quantum randomness is fundamentally different from classical pseudo randomness random number generator qrng hadamard qiskit measurement randomness prerequisites basic python variables functions loops no quantum physics background needed in this guide 10 sections 01 classical vs quantum randomness 02 the simplest qrng 03 multi bit qrng one random byte per shot 04 random integer in a range 05 using real ibm quantum hardware 06 entropy estimation and randomness testing 07 commercial qrng devices 08 one time pad perfect secrecy from perfect randomness 09 summary 10 further reading every time you ask a computer to generate a random number it runs an algorithm that algorithm starts from a seed value and applies a deterministic sequence of operations given the same seed you always get the same sequence this is called a pseudo random number generator prng and it is what powers everything from shuffled playlists to encrypted web traffic quantum computers work differently a qubit in superposition has no predetermined measurement outcome not just unknown but genuinely undetermined when you measure it the universe decides that physical process is the basis for a true random number generator classical vs quantum randomness a classical prng like the mersenne twister is a function f seed sequence the sequence looks random passes statistical tests and is perfectly adequate for most purposes but it is reproducible if an attacker knows the seed or can observe enough output to reconstruct the internal state they can predict future values quantum randomness is different in kind not just degree the born rule states that the probability of measuring a qubit in state 0 or 1 is given by the squared magnitude of the corresponding amplitude there is no hidden variable that predetermined the outcome no seed no internal state to reconstruct the outcome simply did not exist before measurement this distinction matters most in cryptography for generating encryption keys where the entire security of the system rests on the key being unknowable true randomness is worth the overhead common misconception true quantum randomness is not a prerequisite for secure cryptography a well seeded cryptographically secure prng csprng drawing from an operating system entropy pool is sufficient for essentially all practical key generation the real value of a qrng is as a high quality physical entropy source feeding that pool not as a wholesale replacement for the csprng reach for a qrng when you need certified entropy from a characterized physical source or want to reduce reliance on the assumption that an adversary cannot predict or influence your entropy inputs the simplest qrng the simplest quantum random bit requires exactly one gate a hadamard starting from 0 the h gate produces 0 1 sqrt 2 an equal superposition with 50 probability of measuring either outcome from qiskit import quantumcircuit from qiskit_aer import aersimulator single qubit qrng qc quantumcircuit 1 1 qc h 0 put qubit into superposition qc measure 0 0 collapse and record simulator aersimulator job simulator run qc shots 10 result job result counts result get_counts print counts example 0 6 1 4 distribution approaches 50 50 with more shots each shot produces one independent random bit the distribution over many shots should be statistically indistinguishable from a fair coin to extract a single random bit from the results get just the first measurement result job simulator run qc shots 1 counts job result get_counts random_bit int list counts keys 0 print f random bit random_bit multi bit qrng one random byte per shot measuring n qubits in superposition produces n independent random bits simultaneously with 8 qubits you get one random byte per circuit execution from qiskit import quantumcircuit from qiskit_aer import aersimulator def qrng_byte n 8 qc quantumcircuit n n qc h range n all qubits in superposition qc measure range n range n simulator aersimulator job simulator run qc shots 1 result job result bitstring list result get_counts keys 0 qiskit orders bits with qubit 0 on the right random_byte int bitstring 2 return random_byte print qrng_byte a random integer between 0 and 255 each call executes the circuit once and returns a different random byte run it multiple times to collect a stream of random bytes to generate a sequence of random bytes efficiently use multiple shots def qrng_bytes count n 8 qc quantumcircuit n n qc h range n qc measure range n range n simulator aersimulator job simulator run qc shots count counts job result get_counts expand counts dict into a flat list random_bytes for bitstring freq in counts items value int bitstring 2 random_bytes extend value freq return random_bytes count print qrng_bytes 10 example 173 42 209 7 88 251 130 64 19 200 random integer in a range generating a random integer in a b requires enough bits to cover the range then rejection sampling to avoid bias import math def qrng_int a b simulator span b a 1 n_bits math ceil math log2 span while true qc quantumcircuit n_bits n_bits qc h range n_bits qc measure range n_bits range n_bits job simulator run qc shots 1 bitstring list job result get_counts keys 0 value int bitstring 2 reject values outside the range to avoid modulo bias if value span return a value simulator aersimulator random integer between 1 and 100 print qrng_int 1 100 simulator rejection sampling ensures the output distribution is exactly uniform over a b with no bias from truncation using real ibm quantum hardware the aer simulator is deterministic at the algorithmic level it uses classical floating point arithmetic to compute probabilities then samples from them using a classical prng internally simulated qrng is therefore pseudo random under the hood for genuine quantum randomness you need to run on real hardware through ibm quantum from qiskit_ibm_runtime import qiskitruntimeservice samplerv2 as sampler from qiskit import quantumcircuit from qiskit transpiler preset_passmanagers import generate_preset_pass_manager authenticate with your ibm quantum account service qiskitruntimeservice backend service least_busy operational true simulator false n 8 qc quantumcircuit n n qc h range n qc measure range n range n pm generate_preset_pass_manager backend backend optimization_level 1 isa_circuit pm run qc sampler sampler backend job sampler run isa_circuit shots 100 result job result print result 0 data c get_counts on real hardware the randomness originates from quantum measurement shot noise thermal fluctuations and decoherence all contribute additional randomness beyond what the circuit itself generates entropy estimation and randomness testing claiming your generator is random is not the same as demonstrating it the nist statistical test suite sp 800 22 is the standard battery of tests used to evaluate random number generators for cryptographic applications it includes 15 tests covering frequency runs block frequency spectral analysis and serial correlation to test your qrng output informally from collections import counter import math def estimate_entropy bitstring n len bitstring counts counter bitstring entropy sum c n math log2 c n for c in counts values return entropy generate a long bitstring and check entropy bits join str b 2 for b in qrng_bytes 1000 print f entropy per bit estimate_entropy bits 4f ideal 1 0000 a perfect single bit source has entropy of exactly 1 0 bit per bit values below 0 99 suggest bias worth investigating commercial qrng devices several commercial products deliver certified quantum randomness over usb or pcie id quantique quantis uses photon detection on a beam splitter available as a pcie card or usb device certified to ais 31 a german bsi standard and validated under nist sp 800 90b entropy source requirements widely used in financial services and government applications id quantique idq20mc1 an integrated chip qrng designed for embedded systems and iot devices operates at tens of megabits per second quintessencelabs qstream high throughput qrng for data center use delivering gigabit per second random output certified for cryptographic key generation these devices can serve as hardware entropy sources for operating system dev random openssl and key management systems without requiring access to a full quantum computer one time pad perfect secrecy from perfect randomness the one time pad is the only encryption scheme with information theoretic security it is mathematically impossible to crack even with unlimited computing power provided the key is truly random as long as the message and never reused def one_time_pad_encrypt message_bytes key_bytes assert len key_bytes len message_bytes key must be at least as long as message return bytes m k for m k in zip message_bytes key_bytes message b hello key bytes qrng_bytes len message quantum random key ciphertext one_time_pad_encrypt message key plaintext one_time_pad_encrypt ciphertext key xor again to decrypt print f key key hex print f ciphertext ciphertext hex print f decrypted plaintext the catch the key must be distributed securely which is as hard as distributing the message itself in practice quantum key distribution qkd solves the key exchange problem while qrng solves the key generation problem together they form the basis for information theoretically secure communication summary quantum randomness is fundamentally different from classical pseudo randomness it arises from the born rule rather than a deterministic algorithm a hadamard gate plus measurement is all you need to generate a true random bit in qiskit scaling to multi bit output is straightforward and real hardware delivers genuine quantum randomness where simulations cannot for cryptographic applications pair qrng with proper randomness testing before deploying further reading superposition the quantum state that makes qrng possible quantum key distribution secure key exchange to complement qrng key generation born rule the physical law that makes quantum measurement unpredictable was this tutorial helpful yes no share ready to go deeper browse structured courses from coursera edx udemy brilliant and more browse courses related tutorials continue learning with these guides build your first quantum circuit in qiskit complete beginner guide beginner 25 min read read the deutsch jozsa algorithm quantum s first speedup explained beginner 35 minutes read fault tolerant quantum gates why t gates need magic states advanced 22 min read read previous run bigger circuits with qiskit circuit cutting wire gate next introduction to quantum error correction the bit flip code on this page 01 classical vs quantum randomness 02 the simplest qrng 03 multi bit qrng one random byte per shot 04 random integer in a range 05 using real ibm quantum hardware 06 entropy estimation and randomness testing 07 commercial qrng devices 08 one time pad perfect secrecy from perfect randomness 09 summary 10 further reading at a glance level beginner read time 10 min read language python updated jul 2026 related tutorials build your first quantum circuit in qiskit complete beginner guide 25 min read the deutsch jozsa algorithm quantum s first speedup explained 35 minutes fault tolerant quantum gates why t gates need magic states 22 min read courses on this amazon braket learning plan and digital badge aws skill builder free quantum mechanics with sabine paid fundamentals of quantum algorithms ibm learning free get one quantum email a week new tutorials courses worth taking and what changed in qiskit cirq pennylane this week no spam unsubscribe anytime email address subscribe 112 courses 220 tutorials 241 glossary terms 26 framework references 34 case studies quantumcomputing courses com free tutorials curated courses framework 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